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Model-Based Predictive Control Strat...

Morovat, Navid,

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Model-Based Predictive Control Strategies and Renewable Energy Integration for Energy Flexibility Enhancement in School Buildings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Model-Based Predictive Control Strategies and Renewable Energy Integration for Energy Flexibility Enhancement in School Buildings // Navid Morovat.
作者:	Morovat, Navid,
面頁冊數:	1 electronic resource (188 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31238668
ISBN:	9798382314648

Model-Based Predictive Control Strategies and Renewable Energy Integration for Energy Flexibility Enhancement in School Buildings /
Morovat, Navid,

Model-Based Predictive Control Strategies and Renewable Energy Integration for Energy Flexibility Enhancement in School Buildings /Navid Morovat. - 1 electronic resource (188 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

This thesis investigates methods to enhance the energy flexibility potential of school buildings through simulation and experimental studies. It contributes a general methodology for the development of data driven grey-box thermal models and the implementation of model-based predictive control (MPC). The methodology is applied to an archetype fully-electric school building near Montreal, Quebec, Canada. This approach is scalable and transferable to other institutional or mid-size commercial buildings.To streamline the implementation of MPC, the proposed approach employs grey-box low-order resistance-capacitance (RC) thermal network models, a clustering of weather conditions to identify typical anticipated scenarios, and several near-optimal setpoint profiles corresponding to each cluster. Archetype control-oriented models for zones with convective systems and zones with radiant floor systems are developed and calibrated with measured data. The calibrated models are used to apply MPC to the school building using the established dynamic tariffs for morning and evening peaks. For the experimental study, the developed MPC framework is applied in six classrooms, and the results are compared with four classrooms with the reactive control system as reference cases. The energy flexibility is quantified based on a proposed building energy flexibility index (BEFI). Results indicated that the school building can provide 45% to 95% energy flexibility (load shifting relative to reference) during on-peak hours while satisfying thermal comfort constraints.Finally, this thesis presents an MPC methodology for the integration of air-based photovoltaic/thermal (PV/T) systems to further enhance the energy flexibility in school buildings so that in addition to the production of solar electricity, they can be used to preheat fresh air for the classrooms during the heating season. A data-driven grey-box model for the classrooms is calibrated with measured data, and a PV/T model as a renewable energy retrofit measure for energy efficiency and flexibility is developed. These models are integrated to apply MPC and reduce peak demand during morning and evening. Results show that using an MPC along with PV/T integration can significantly reduce peak demand during morning and evening high demand periods for the grid. The proposed methodology helps institutional buildings to facilitate their integration into future smart grids and smart cities.

English

ISBN: 9798382314648Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

School buildings

Model-Based Predictive Control Strategies and Renewable Energy Integration for Energy Flexibility Enhancement in School Buildings /
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520 $a This thesis investigates methods to enhance the energy flexibility potential of school buildings through simulation and experimental studies. It contributes a general methodology for the development of data driven grey-box thermal models and the implementation of model-based predictive control (MPC). The methodology is applied to an archetype fully-electric school building near Montreal, Quebec, Canada. This approach is scalable and transferable to other institutional or mid-size commercial buildings.To streamline the implementation of MPC, the proposed approach employs grey-box low-order resistance-capacitance (RC) thermal network models, a clustering of weather conditions to identify typical anticipated scenarios, and several near-optimal setpoint profiles corresponding to each cluster. Archetype control-oriented models for zones with convective systems and zones with radiant floor systems are developed and calibrated with measured data. The calibrated models are used to apply MPC to the school building using the established dynamic tariffs for morning and evening peaks. For the experimental study, the developed MPC framework is applied in six classrooms, and the results are compared with four classrooms with the reactive control system as reference cases. The energy flexibility is quantified based on a proposed building energy flexibility index (BEFI). Results indicated that the school building can provide 45% to 95% energy flexibility (load shifting relative to reference) during on-peak hours while satisfying thermal comfort constraints.Finally, this thesis presents an MPC methodology for the integration of air-based photovoltaic/thermal (PV/T) systems to further enhance the energy flexibility in school buildings so that in addition to the production of solar electricity, they can be used to preheat fresh air for the classrooms during the heating season. A data-driven grey-box model for the classrooms is calibrated with measured data, and a PV/T model as a renewable energy retrofit measure for energy efficiency and flexibility is developed. These models are integrated to apply MPC and reduce peak demand during morning and evening. Results show that using an MPC along with PV/T integration can significantly reduce peak demand during morning and evening high demand periods for the grid. The proposed methodology helps institutional buildings to facilitate their integration into future smart grids and smart cities.
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